1.1 Data Preparation

raw.data <- import("https://raw.githubusercontent.com/Zen-Rofiqy/STA1352-RegSpas/main/Tugas%20Akhir/Data.csv")
peta <- readOGR(dsn ="Sumut", layer = "Sumatera_Utara_ADMIN_BPS")

## OGR data source with driver: ESRI Shapefile 
## Source: "C:\Users\Fathan\Documents\Obsidian Vault\2. Kuliah\Smt 5\4. Regresi Spasial\Tugas Akhir\Sumut", layer: "Sumatera_Utara_ADMIN_BPS"
## with 34 features
## It has 6 fields

peta <- peta[-which(peta@data$Kabupaten == "Danau Toba"), ]

datatable(raw.data, filter = 'top', 
          options = list(pageLength = 5))

str(raw.data)

## 'data.frame':    33 obs. of  16 variables:
##  $ ID_KAB: int  1208 1219 1210 1212 1215 1211 1276 1278 1275 1277 ...
##  $ KAB   : chr  "Asahan" "Batu Bara" "Dairi" "Deli Serdang" ...
##  $ LAT   : num  2.85 3.23 2.84 3.48 2.24 ...
##  $ LONG  : num  99.5 99.5 98.3 98.7 98.6 ...
##  $ y     : num  8.64 11.53 7.88 3.62 8.86 ...
##  $ x1    : num  2 18.4 12.4 1.4 16.4 16.8 0.9 3.4 0.5 15.8 ...
##  $ x2    : int  671 372 247 702 265 468 225 119 821 199 ...
##  $ x3    : int  1119177 1109844 1023193 1207845 993122 1328079 1221962 858826 1851065 1192550 ...
##  $ x4    : int  230176 246179 125512 306494 110126 75705 76342 73109 259011 57641 ...
##  $ x5    : num  8.82 8.26 9.72 10.27 10 ...
##  $ x6    : num  71.1 69.5 72.6 76.2 70.3 ...
##  $ x7    : num  6.26 6.21 0.88 8.79 0.42 2.71 6.36 3.65 8.89 7.76 ...
##  $ x8    : int  11515 10755 10740 12657 8250 12554 11358 8434 15503 11337 ...
##  $ x9    : num  65.7 73.5 85 66.8 85.8 ...
##  $ x10   : num  68.7 67.5 69.6 72.1 70 ...
##  $ x11   : int  51781 28259 11613 124336 8351 22084 20470 5611 95342 13733 ...

sum(is.na(raw.data))

## [1] 0

raw.data[!complete.cases(raw.data),]

##  [1] ID_KAB KAB    LAT    LONG   y      x1     x2     x3     x4     x5    
## [11] x6     x7     x8     x9     x10    x11   
## <0 rows> (or 0-length row.names)

data <- raw.data

2.1 Sebaran data peubah y dan x

y <-
ggplot(data, aes(x="", 
                     y=y)) +
  #Violin
  geom_violin(scale="count", fill='#EE6C3A', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#EE6C3A', color='black',
                     outlier.size=4, outlier.color='#EE6C3A', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "Y") + theme1.1 

x1 <-
ggplot(data, aes(x="", 
                     y=x1)) +
  #Violin
  geom_violin(scale="count", fill='#A12B5E', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#A12B5E', color='black',
                     outlier.size=4, outlier.color='#A12B5E', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X1") + theme1.1 

x2 <-
ggplot(data, aes(x="", 
                     y=x2)) +
  #Violin
  geom_violin(scale="count", fill='#EB1948', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#EB1948', color='black',
                     outlier.size=4, outlier.color='#EB1948', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X2") + theme1.1 

x3 <-
ggplot(data, aes(x="", 
                     y=x3)) +
  #Violin
  geom_violin(scale="count", fill='#6F2062', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#6F2062', color='black',
                     outlier.size=4, outlier.color='#6F2062', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X3") + theme1.1 

x4 <-
ggplot(data, aes(x="", 
                     y=x4)) +
  #Violin
  geom_violin(scale="count", fill='#263574', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#263574', color='black',
                     outlier.size=4, outlier.color='#263574', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X4") + theme1.1 

x5 <-
ggplot(data, aes(x="", 
                     y=x5)) +
  #Violin
  geom_violin(scale="count", fill='#1A1334', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#1A1334', color='black',
                     outlier.size=4, outlier.color='#1A1334', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X5") + theme1.1 

x6 <-
ggplot(data, aes(x="", 
                     y=x6)) +
  #Violin
  geom_violin(scale="count", fill='#FBBF54', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#FBBF54', color='black',
                     outlier.size=4, outlier.color='#FBBF54', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X6") + theme1.1 

x7 <-
ggplot(data, aes(x="", 
                     y=x7)) +
  #Violin
  geom_violin(scale="count", fill='#055559', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#055559', color='black',
                     outlier.size=4, outlier.color='#055559', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X7") + theme1.1 

x8 <-
ggplot(data, aes(x="", 
                     y=x8)) +
  #Violin
  geom_violin(scale="count", fill='#ABD36D', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#ABD36D', color='black',
                     outlier.size=4, outlier.color='#ABD36D', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X8") + theme1.1 

x9 <-
ggplot(data, aes(x="", 
                     y=x9)) +
  #Violin
  geom_violin(scale="count", fill='#37B883', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#37B883', color='black',
                     outlier.size=4, outlier.color='#37B883', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X9") + theme1.1 

x10 <-
ggplot(data, aes(x="", 
                     y=x10)) +
  #Violin
  geom_violin(scale="count", fill='#26294A', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#26294A', color='black',
                     outlier.size=4, outlier.color='#26294A', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X10") + theme1.1 

x11 <-
ggplot(data, aes(x="", 
                     y=x11)) +
  #Violin
  geom_violin(scale="count", fill='#047453', color=NA, alpha=0.15, 
                    trim = FALSE) +
  #Boxplot
  geom_boxplot(fill='#047453', color='black',
                     outlier.size=4, outlier.color='#047453', 
                     notch=T, width=0.2) +
  #Mean Marker
  stat_summary(fun = mean, geom = "point", shape = 16, size = 4, 
               color = "white") +
  theme(plot.title = element_text(hjust=0.5),legend.position = "none") +
  labs(x = "\n", 
       y='',
       title = "X11") + theme1.1 

chart <-
  plot_grid(y, x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11,
            nrow=2)
chart

ggsave("01_Sebaran Data.png", chart, path = export.chart,
        dpi = 300, height = 20, width = 40)

2.2 Hubungan antar peubah

Correlation Plot

install_load('scales')
m <- cor(data[,5:(ncol(data))]) #Membuat matriks korelasi

colors <- alpha(colorRampPalette(c("#D89534", #Warna Bawah
                                   "#DEDBCE", #Warna Tengah
                                   "#4EC2C1"))(10), #Warna Atas
                                   alpha = 0.95) #Transparan

#Corrplot Elips
corrplot(m, method = 'color',  addCoef.col="black",  diag=FALSE, type='lower', 
         tl.col = "black", col=colors, bg="white", pch="+")

## Corr plot
install_load('metan')
plot(corr_coef(data[,5:(ncol(data))]), reorder = FALSE)

Scatterplot

install_load('DescTools')
pairs(m, pch = 19, lower.panel = NULL, col = "#37B883")

2.3 Choropleth Map

install_load('RColorBrewer')

peta$y <- data$y
my.palette <- brewer.pal(n = 9, name = "OrRd")
spplot(peta, "y", col.regions=my.palette, cuts = 7, col = "black", 
       main=list(label="Sebaran Persentase Kemiskinan di Sumatera Utara Tahun 2022\n", 
                 cex=2) )